Detection of sound activity

• US 20020165713A1
• Filed: 12/04/2001
• Published: 11/07/2002
• Est. Priority Date: 12/04/2000
• Status: Active Grant

• Current Assignee: AB GRUNDSTENEN 91089, Google LLC (Alphabet Inc.)
• Original Assignee: Global IP Sound AB (Alphabet Inc.)
• Inventors: Skoglund, Jan K., Linden, Jan T.
• Granted Patent: US 6,993,481 B2
• Time in Patent Office: Days
• US Class Current: 704/240
• CPC Class Codes: G10L 25/78 Detection of presence or absence of voice signals switching of direction of transmission by voice frequency in two-way loud-speaking telephone systems H04M9/10

Abstract

According to the invention, a method for detecting speech activity for a signal is disclosed. In one step, a plurality of features is extracted from the signal. An active speech probability density function (PDF) of the plurality of features is modeled, and an inactive speech PDF of the plurality of features is modeled. The active and inactive speech PDFs are adapted to respond to changes in the signal over time. The signal is probability-based classifyied based, at least in part, on the plurality of features. Speech in the signal is distinguished based, at least in part, upon the probability-based classification.

Claims

1. A method for detecting speech activity for a signal, the method comprising the steps of:

extracting a plurality of features from the signal;

modeling a first and a second probability density functions (PDFs) of the plurality of features, wherein;

the first PDF models active speech conditions for the signal, and

the second PDF models inactive speech conditions for the signal;

adapting the first and second PDFs to respond to changes in the signal over time;

probability-based classifying of the signal based, at least in part, on the plurality of features; and

distinguishing speech in the signal based, at least in part, upon the probability-based classifying step.

2. The method for detecting speech activity for the signal as recited in claim 1, wherein the probability-based classifying step uses the first and second PDFs.

3. The method for detecting speech activity for the signal as recited in claim 1, wherein the modeling step comprises a step of determining a mathematical model for the signal from the plurality of features.

4. The method for detecting speech activity for the signal as recited in claim 1, wherein the adapting step comprises a step of increasing a likelihood.

5. The method for detecting speech activity for the signal as recited in claim 1, wherein the adapting step comprises a step of identifying extreme values in a long sequence of previous frames.

6. The method for detecting speech activity for the signal as recited in claim 1, wherein the probability-based classifying step comprises a step of classifying based on likelihood ratio detection.

7. The method for detecting speech activity for the signal as recited in claim 1, wherein the probability-based classifying step comprises applying a log-likelihood ratio test to one of the plurality of features.

8. The method for detecting speech activity for the signal as recited in claim 1, wherein at least one of the first and second PDFs comprises a Gaussian mixture model.

9. The method for detecting speech activity for the signal as recited in claim 1, wherein at least one of the first and second PDFs uses a non-Gaussian model.

10. The method for detecting speech activity for the signal as recited in claim 1, wherein at least one of the first and second PDFs comprises a plurality of basic density models.

11. The method for detecting speech activity for the signal as recited in claim 1, wherein at least one of the plurality of features is related to power in a spectral band of the signal.

12. The method for detecting speech activity for the signal as recited in claim 1, further comprising a step of smoothing an activity decision for hangover periods to produce a smoothed activity decision.

13. A computer-readable medium having computer-executable instructions for performing the computer-implementable method for detecting speech activity for the signal of claim 1.

15. The method for detecting sound activity for the signal as recited in claim 14, wherein the probability-based classifying step uses the active and inactive speech PDFs.

16. The method for detecting sound activity for the signal as recited in claim 14, wherein the adapting step comprises a step of increasing a likelihood.

17. The method for detecting sound activity for the signal as recited in claim 14, wherein at least one of the active and inactive speech PDFs uses a non-Gaussian model.

18. A computer-readable medium having computer-executable instructions for performing the computer-implementable method for detecting sound activity for the signal of claim 14.

20. The method for detecting sound activity for the signal as recited in claim 19, wherein both the active and inactive speech PDFs use a non-Gaussian model.

14. A method for detecting sound activity for a signal, the method comprising the steps of:

extracting a plurality of features from the signal;

modeling an active speech probability density function (PDF) of the plurality of features;

modeling an inactive speech PDF of the plurality of features;

adapting the active and inactive speech PDFs to respond to changes in the signal over time;

probability-based classifying of the signal based, at least in part, on the plurality of features; and

distinguishing speech in the signal based, at least in part, upon the probability-based classifying step.

19. A method for detecting sound activity for a signal, the method comprising the steps of:

extracting a plurality of features from the signal;

modeling an active speech probability density function (PDF) of the plurality of features;

modeling an inactive speech PDF of the plurality of features, wherein at least one of the active and inactive speech PDFs uses a non-Gaussian model;

adapting the active and inactive speech PDFs to respond to changes in the signal over time;

probability-based classifying of the signal based, at least in part, the active and inactive speech PDFs; and

distinguishing speech in the signal based, at least in part, upon the probability-based classifying step.

21. A computer-readable medium having computer-executable instructions for performing the computer-implementable method for detecting sound activity for the signal of claim 19.